Developing Data Science Approaches to Improve Paediatric Critical Care Patient Flows and its Related Health Economics Benefits in Scotland

开发数据科学方法以改善苏格兰儿科重症监护患者流量及其相关的健康经济效益

• 批准号: 2444385
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444385
• 关键词: Developing; Data; Science; Approaches; Improve

Paediatric Critical Care (PCC) is an essential service providing health care to critically ill children. In Scotland the PCC service is delivered by two Paediatric Critical Care Units (PCCU); one in Edinburgh, and one in Glasgow. In recent years there has been increasing demand for PCCU beds with a PCC bed crisis being reported. In cases where the critical care units run out of available beds, patients must be diverted to a unit with an available bed or urgent care must be delayed directly impacting patient care. Reasons for the increasing demand for PCCU beds range from seasonal respiratory infections to delays in patient discharges as well as changes in PCC patient characteristics. Bed usage and staffing data is routinely collected from the PCCUs as well as individual patient level data including admission, discharge and physiological data. This gives a unique opportunity to develop powerful data driven tools which could be used to optimise and improve patient flow and ultimately patient care in the PCC. The ability to simulate patient flow through a PCCU could provide valuable insights into operational bottlenecks and inefficiencies. Simulation models such as Discrete Event Simulation (DES), System Dynamics (SD) and Agent Based Systems (ABS) within healthcare are well established with proven benefits once developed [1][2][3]. An accurate and representative simulation gives the opportunity to test possible scenarios for improvement before integration into hospital systems unlike other approaches which typically rely heavily on expert opinion as an approach towards improvement. In recent years Machine Learning (ML) coupled with the availability of large datasets and increase in computational performance has revolutionised many domains. In healthcare this is no different, with the prediction of patient care trajectories using ML becoming an increasingly active area of research [4][5]. With the wealth of data routinely available from the PCCUs at an individual patient level, the development of machine learning models which can predict a probable care trajectory for how a patient may transition through the hospital based on a patient physiological phenotype could be employed to plan future care and improve patient flow.This project will develop a simulation model which can be used to simulate patient flow in the PCCUs, identify bottlenecks and then test solutions for improvement through simulating the proposed scenarios. Secondarily machine learning methods will be developed which allow for the prediction of patient care trajectories from routinely collected clinical data to aid in the planning of patient care. Finally, it will include a thorough health economic analysis to fully quantify the impact this data driven approach brings.References[1] Kusum S Mathews and Elisa F Long. A conceptual framework for improving critical care patient flow and bed use. Annals of the American Thoracic Society, 12(6):886-894, 2015.[2] Syed Mohiuddin, John Busby, Jelena Savovic, Alison Richards, Kate Northstone, William Hollingworth, Jenny L Donovan, and Christos Vasilakis. Patient flow within UK emergency departments: a systematic review of the use of computer simulation modelling methods. BMJ open, 7(5), 2017.[3] Eduardo Cabrera, Manel Taboada, Ma Luisa Iglesias, Francisco Epelde, and Emilio Luque.Optimization of healthcare emergency departments by agent-based simulation. Procedia computer science, 4:1880-1889, 2011.[4] Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of biomedical informatics, 69:218-229, 2017.[5] Hongteng Xu, Weichang Wu, Shamim Nemati, and Hongyuan Zha. Patient flow prediction via discriminative learning of mutually-correcting processes. IEEE transactions on Knowledge and Data Engineering, 29(1):157-171, 2016.

儿科重症监护（PCC）是为重症儿童提供医疗保健的基本服务。在苏格兰，PCC服务由两个儿科重症监护室（PCCU）提供;一个在爱丁堡，一个在格拉斯哥。近年来，对PCCU床位的需求不断增加，据报道PCC床位危机。在重症监护病房的可用床位不足的情况下，患者必须转移到有可用床位的病房，或者必须延迟紧急护理，直接影响患者护理。对PCCU病床需求增加的原因包括季节性呼吸道感染、患者出院延迟以及PCC患者特征的变化。定期从PCCU收集床位使用和人员配备数据以及个体患者水平数据，包括入院、出院和生理数据。这为开发强大的数据驱动工具提供了独特的机会，这些工具可用于优化和改善PCC中的患者流程并最终改善患者护理。通过PCCU模拟患者流的能力可以为运营瓶颈和效率低下提供有价值的见解。仿真模型，如离散事件仿真（DES），系统动力学（SD）和基于智能体的系统（ABS）在医疗保健领域已经建立良好，一旦开发出来就有证明的好处[1][2][3]。一个准确的和有代表性的模拟提供了机会，以测试可能的情况下，改善集成到医院系统之前，不像其他方法，通常严重依赖于专家的意见，作为一种改进的方法。近年来，机器学习（ML）加上大数据集的可用性和计算性能的提高已经彻底改变了许多领域。在医疗保健领域也是如此，使用ML预测患者护理轨迹成为越来越活跃的研究领域[4][5]。由于PCCU在单个患者层面上常规提供的数据丰富，可以开发机器学习模型，该模型可以根据患者的生理表型预测患者如何通过医院过渡的可能护理轨迹，可以用于规划未来的护理和改善患者流量。本项目将开发一个模拟模型，用于模拟PCCU中的患者流量，识别瓶颈，然后通过模拟建议的场景测试改进方案。其次，将开发机器学习方法，允许从常规收集的临床数据中预测患者护理轨迹，以帮助规划患者护理。最后，它将包括一个全面的卫生经济分析，以充分量化这种数据驱动的方法带来的影响。参考文献[1] Kusum S马修斯和Elisa F龙。改善重症监护病人流量和病床使用的概念框架。美国胸科学会年鉴，12（6）：886-894，2015年。[2]Syed Mohiuddin，John Busby，Jelena Savovic，Alison理查兹，Kate Northstone，William Hollingworth，Jenny L Donovan和Christine Vasilakis。英国急诊部门内的患者流量：计算机模拟建模方法使用的系统综述。BMJ公开赛，7（5），2017年。[3]Eduardo Cabrera，Manel Taboada，Ma Luisa Iglesias，弗朗西斯科Epelde和Emilio Luque。通过基于代理的模拟优化医疗急救部门。Procedia计算机科学，4：1880-1889，2011。[4]Trang Pham，Truyen Tran，Dinh Phung，and Svetha Venkatesh.从医疗记录中预测医疗保健轨迹：深度学习方法。生物医学信息学杂志，69：218-229，2017。[5]Hongteng Xu，Weichang Wu，Shamim Nemati，and Hongyuan Zha.通过相互校正过程的判别学习进行患者流量预测。IEEE transactions on Knowledge and Data Engineering，29（1）：157-171，2016。